Meta-Llama-3_3-70B-Instruct

Large Language Models (LLM)

Llama 3.3 es un modelo de lenguaje generativo ajustado por instrucciones optimizado para casos de uso de diálogo multilingüe. Fue lanzado por Meta AI el 6 de diciembre de 2024, y utiliza una arquitectura avanzada de transformador, además de estar diseñado para alinearse con las preferencias humanas en cuanto a utilidad y seguridad mediante ajuste supervisado (SFT) y aprendizaje por refuerzo con retroalimentación humana (RLHF). La fecha de corte de conocimiento para este modelo 70B es el 31 de diciembre de 2023.

Prueba otro modelo

Acerca del modelo Meta-Llama-3_3-70B-Instruct

Publicado el huggingface

06/12/2024

Licencia: Llama 3.3 Community

Precio de entrada

0.67 € /Mtoken(entrada)

Precio de salida

0.67 € /Mtoken(salida)

Características soportadas

Llamada a funciónStreaming

Formatos de salida

raw_textjson_objectjson_schema

Tamaños de contexto

131k

Parámetros

70B

Prueba el modelo jugando con él.

Example of how to use Meta-Llama-3_3-70B-Instruct API in python

With a simple HTTP client (requests)

First install the requests library:

pip install requests

Next, export your access token to the OVH_AI_ENDPOINTS_ACCESS_TOKEN environment variable:

export OVH_AI_ENDPOINTS_ACCESS_TOKEN=<your-access-token>

If you do not have an access token key yet, follow the instructions in the AI Endpoints – Getting Started.

Finally, run the following Python code:

import os
import requests
 
url = "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1/chat/completions"
payload = {
    "max_tokens": 512,
    "messages": [
        {
            "content": "Explain gravity to a 6 years old",
            "role": "user"
        }
    ],
    "model": "Meta-Llama-3_3-70B-Instruct",
    "temperature": 0,
}
 
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {os.getenv('OVH_AI_ENDPOINTS_ACCESS_TOKEN')}",
}
 
response = requests.post(url, json=payload, headers=headers)
if response.status_code == 200:
    # Handle response
    response_data = response.json()
    # Parse JSON response
    choices = response_data["choices"]
    for choice in choices:
        text = choice["message"]["content"]
        # Process text and finish_reason
        print(text)
else:
    print("Error:", response.status_code, response.text)

With the Python OpenAI library

The Meta-Llama-3_3-70B-Instruct API is compatible with the OpenAI specification.

First install the openai library:

pip install openai

Next, export your access token to the OVH_AI_ENDPOINTS_ACCESS_TOKEN environment variable:

export OVH_AI_ENDPOINTS_ACCESS_TOKEN=<your-access-token>

Finally, run the following Python code:

import os

from openai import OpenAI

url = "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1"

client = OpenAI(
    base_url=url,
    api_key=os.getenv("OVH_AI_ENDPOINTS_ACCESS_TOKEN")
)

def chat_completion(new_message: str) -> str:
    history_openai_format = [{"role": "user", "content": new_message}]

    return client.chat.completions.create(
        model="Meta-Llama-3_3-70B-Instruct",
        messages=history_openai_format,
        temperature=0,
        max_tokens=1024
    ).choices.pop().message.content


if __name__ == '__main__':
    print(chat_completion("Explain gravity for a 6 years old"))  

Model rate limit

When using AI Endpoints, the following rate limits apply:

Anonymous: 2 requests per minute, per IP and per model.
Authenticated with an API access key: 400 requests per minute, per Public Cloud project and per model.

If you exceed this limit, a 429 error code will be returned.

If you require higher usage, please get in touch with us to discuss increasing your rate limits.

Going Further

Want to explore the full capabilities of the LLM API? Dive into our dedicated Structured Output and Function Calling guides.

For a broader overview of AI Endpoints, explore the full AI Endpoints Documentation.

Reach out to our support team or join the OVHcloud Discord #ai-endpoints channel to share your questions, feedback, and suggestions for improving the service, to the team and the community.

Meta-Llama-3_3-70B-Instruct